Data collected in three phases of product design (genealogy data), manufacturing (test and repair data), and customer feedback (product return and maintenance data) need to be jointly analyzed to detect problems and causing factors, find solutions based on historical data, and predict the performance of the system upon any changes in the involving factors. Such analysis leads towards improving time, cost, and quality of the tasks performed in each of the above phases.
Currently, test engineers, production planners, and maintenance engineers perform different levels of analysis on the available data ranging from simple reporting to advanced statistical analysis. The level of analysis depends on the business needs and familiarity of the user with analytics procedures and tools. The following are typical shortcomings of current approaches taken by users:
Since users are limited to reporting tools, they tend to ignore the value of data insights.
The analysis of manufacturing test, return, and product genealogy data is typically performed manually or semi-automatically using, for example, Excel™, Minitab™, or any other similar rudimentary software. Users who value data insights may use various data export tools to download data to their desktop machines and use customized templates available on their desktops to analyze the data by applying filters, sorting, and creating charts. This process is a time consuming process with many deficiencies that does not guarantee to find the problem or suggest a proper solution in a reasonable time.
Sometimes, off-the-shelf Business Intelligence (BI) tools are used to analyze the data. General-purpose BI tools use the various databases that store the desired data and provide features to create data dictionaries, data cubes, various charts, and brows multiple levels of data through well-designed user interfaces. However, directed questions that are specific to a data domain would be impossible or require sophisticated steps that makes the process cumbersome and therefore the users are reluctant to use them.
Therefore, current practices are characterized as time consuming and deficient processes in obtaining deep analytics for test, return and repair, and supply chain analysis.
The vast volume of data collected during test and field study processes requires deep data analysis to find the relations between involving factors, investigate the source of time and cost consuming processes, and study the alternative methods to increase the profit. The invention addresses this issue by demonstrating high speed, accuracy, and usability characteristics.